“By directly printing ionic liquid sensors within these soft systems, we open new avenues to devise design and fabrication that will ultimately allow true closed-loop control of soft robots,” said project researcher Dr. Michael Wehrner.

Each T-shaped SSA consists of three matrix layers made of an elastic polymer (elastomer), namely the dorsal, actuator, and anterior matrices. Each of these matrices contains a conductive ionogel “sensor” ink.

The dorsal matrix contains the curvature sensor ink, the actuator matrix contains the inflation sensor ink with spacers and a network of bladders that fill up with air, while the anterior matrix contains the contact sensor, attached to a set of electrical leads.

The curvature sensor correlates with SSA displacement, the inflation sensor indicates whether or not the displacement is intentional (i.e. the bladders are filled up intentionally), and the contact sensor indicates when the SSA is in contact with an object.

Materials for three parts of the matrix (dorsal, actuator, and anterior) and two (sensor and fugitive) inks were prepared and 3D printed using a custom-built multi-material ABG 10000 3D printer from Aerotech Inc. into a mold assembly.

After printing was complete, the matrix materials were crosslinked and each SSA was removed from the mold assembly. Once the SSAs were cooled, the fugitive ink was removed and in its place, a stainless steel nozzle with Luer lock fixture and electrical leads were inserted through the actuator matrix into the inlets to the actuator network and all sensors.

Each SSA consists of a resistive strain gauge, and since the SSA bends freely in a semi-circular way when inflated, anything obstructing this bending will make the inflation, curvature, and contact sensors increase the electrical resistance of the SSA, causing it to bend more.

This can be applied to a robotic arm gripper, which grabs objects tighter when any of the SSA arms are obstructed, since more resistance to the current flowing through it, the more air enters the bladders.

Researcher Robert Wood, who is the Charles River Professor of Engineering and Applied Sciences at Harvard said:

“The techniques developed in the Lewis Lab have the opportunity to revolutionize how robots are created — moving away from sequential processes and creating complex and monolithic robots with embedded sensors and actuators.”

Next, the researchers hope to harness the power of machine learning to train these devices to grasp objects of varying size, shape, surface texture, and temperature.